Robust and precise localization is needed for all modules of autonomous vehicle software, such as path planning, trajectory following, or object prediction. To enable full self-driving capabilities, a reliance on satellite-based global localization systems such as the Global Positioning System (GPS) is not feasible. First, such sensors are expensive; second, sensor dropouts can lead to subsequent system failures. Therefore, simultaneous localization and mapping (SLAM) algorithms are important in developing robots and autonomous vehicles [1]}, [2]}. These algorithms can provide a precise robot pose by only using sensor measurements of the environment and therefore provide an important localization technique. To achieve robust SLAM algorithms, it is necessary to include the environmental information from various sensors such as LiDARs and cameras. Since all sensors have their own individual advantages and disadvantages, sensor fusion can be a huge benefit for the localization of autonomous vehicles [3]}.
